Between 1972 and 2013, the United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) jointly launched eight Landsat satellites that have, to date, acquired the most comprehensive land remote sensing data of our planet (Markham et al., 2012; Markham and Helder, 2012). Dubbed the Landsat program, its sensors have evolved from the Multi-Spectral Scanner (MSS) (Landsat 1–3) to Thematic Mapper (TM) (Landsat 4–5), then the Enhanced TM (ETM+) (Landsat 6–7) and now the Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS) (Landsat 8). The MSS (1–3) provided data in four spectral channels (green, red, NIR-1, NIR-2) at a spatial resolution of 80 m.

Landsat TM (4–5) satellites were designed to extend the spectral coverage of Landsat MSS to the Shortwave (SWIR) section of the EM spectrum, while improving the spatial resolution to 30 m. A thermal infrared (TIR) band was also introduced with a spatial resolution of 120 m. The Landsat ETM+ (7) sensor was designed to maintain all the characteristics of TM while introducing a 15 m panchromatic band. Landsat OLI (8) collects data in the same spectral bands as ETM+ but in slightly modified wavelengths of the EM spectrum (Irons et al., 2012; Roy et al., 2014). Additional bands include a coastal/aerosol band, a cirrus band and a quality assurance band that provides information on the presence of features such as clouds and terrain occlusions.

With the exception of Landsat 6, which did not achieve orbit upon its launch in 1993, all Landsat satellites have over the years acquired tremendous amounts of data of the earth. The first seven satellites operated with the whiskbroom design, while Landsat 8 uses the pushbroom imaging design. Landsat satellites are in a sun-synchronous orbit with a revisit time between 16 and 18 days. Table 3.2 presents the satellites, their sensors, launch dates and links to other relevant information.

Landsat data became free of charge in December 2008 when the USGS opened its data archive to the world. Thus, all Landsat data can now be downloaded from a number of online data repositories hosted by the USGS. These include: GLOVIS, REVERB and Earth Explorer. A user must register before requesting and downloading data.

Landsat images have been used in a wide range of fields including agriculture, geology, forestry, regional planning, education, mapping, global change research, emergency response and disaster relief. In data-poor regions such as Africa and Asia, Landsat has been the main source of remote sensing data for numerous analyses. Apart from being free, the large footprint of Landsat images (each covering about 185 × 185 km on the ground) has been an important advantage. For example, about nine RapidEye images are required to analyze the same area (in extent) as a single Landsat image. In the case of DigitalGlobe, more than 80 scenes are required. Thus, the use of Landsat images permits very large (and inaccessible) areas to be easily analyzed. Yet, its spatial and temporal resolution is a limitation in certain areas and/or applications.

In SHA areas such as West Africa, Landsat’s spatial resolution has been a limitation in capturing the small agricultural plots. Its temporal resolution, coupled with excessive cloud cover has also largely prevented mapping the spatial distribution of different crops in such environments (Forkuor, 2014). Consequently, many studies that used Landsat images for land use and land cover mapping classified only cropland, without further subdivision (Paré et al., 2008; Tappan et al., 2000; Vittek et al., 2014).

Image fusion approaches have often been used to overcome the above-mentioned spatial and temporal resolution limitations of Landsat. In terms of temporal resolution, spatiotemporal fusion models are used to combine the high spatial resolution of Landsat with low spatial but high temporal resolution images such as MODIS (moderate resolution image spectroradiometer) and MERIS to generate synthetic Landsat images. Proposed fusion models include the spatial and temporal adaptive reflectance fusion model (STARFM) (Gao et al., 2006; Hilker et al., 2009), the enhanced STARFM (ESTARFM) (Zhu et al., 2010), the modified ESTARFM (mESTARFM) (Fu et al., 2013) and the spatiotemporal image fusion model (SPI-FM) (Hazaymeh and Hassan, 2015). Several studies have demonstrated the advantages of such approaches in, for example, improving crop monitoring using Landsat images (Amorós-López et al., 2013; Hazaymeh and Hassan, 2015; Lobell and Asner, 2004; Thenkabail and Wu, 2012; Watts et al., 2011). In terms of spatial resolution, (Song et al., 2015) investigated the possibilities of improving the spatial resolution of Landsat by fusing it with SPOT5 imagery using a learning based super-resolution method. The objective of the, fusion was to generate an output with the large spatial footprint of Landsat and the high spatial resolution of SPOT5. Such approaches can be investigated to determine whether very high resolution images (e.g., from DigitalGlobe) can be fused with large footprint images like Landsat and Sentinel-2.

Further details of the Landsat program, including fact sheets, can be found here.